Prediction of lime quality in lime baking furnaces using neural fuzzy methods

Optimizing the quality of lime, while using the energy system in the lime baking oven is a great service. Since lime baking is always allowed, you can easily control it. The purpose of this study was to predict the quality of lime during the manufacturing process in a lime baking oven and adjust the parameters provided before service delivery. The system variables presented in this study include: input tonnage and parameters of each round. Improper adjustment of these parameters will result in increased fuel consumption, resulting in poor quality lime production. Accordingly, in this paper, artificial neural networks as well as fuzzy neural networks have been used as predictive tools to predict the quality of lime produced during the baking process. These parameters are feeder, idle furnace, preheater, air conditioner, furnace, time and fuel consumption and output of the produced lime quality model. Modeling in matlab software (matlab2017) was performed using 472 samples with 8 properties. Eighty percent of the samples were used for training and 20% for testing. At the end of modeling, artificial neural networks error 0.066 and fuzzy neural network error 0.054 were obtained.